GeoSimcond: Conditional (fast) simulation of Gaussian and non Gaussian...

In GeoModels: Procedures for Gaussian and Non Gaussian Geostatistical (Large) Data Analysis

Conditional (fast) simulation of Gaussian and non Gaussian Random Fields.

Description

Simulates spatial (spatio-temporal) Gaussian and non-Gaussian random fields conditioned on observed data using specified correlation models.

Usage

GeoSimcond(estobj = NULL, data, coordx, coordy = NULL, coordz = NULL, coordt = NULL,
           coordx_dyn = NULL, corrmodel, distance = "Eucl", grid = FALSE, loc,
           maxdist = NULL, maxtime = NULL, method = "Cholesky", model = "Gaussian",
           n = 1, nrep = 1, local = FALSE, L = 1000, neighb = NULL,
           param, anisopars = NULL, radius = 6371, sparse = FALSE, time = NULL,
           copula = NULL, X = NULL, Xloc = NULL, Mloc = NULL,
           parallel=FALSE, ncores = NULL)

Arguments

estobj	Object of class Geofit containing model information
data	Numeric vector/matrix/array of observed data
coordx	Numeric matrix of spatial coordinates (d x 2 or d x 3)
coordy	Optional numeric vector of y-coordinates
coordz	Optional numeric vector of z-coordinates
coordt	Optional numeric vector of temporal coordinates
coordx_dyn	Optional list of dynamic spatial coordinates
corrmodel	String specifying correlation model name
distance	String specifying distance metric (default: "Eucl")
grid	Logical for regular grid (default: FALSE)
loc	Numeric matrix of prediction locations (n x 2)
maxdist	Optional maximum distance for local kriging
maxtime	Optional maximum temporal distance
method	String for decomposition method ("Cholesky", "TB", or "CE")
model	String specifying random field type (default: "Gaussian")
n	Number of trials for binomial RFs (default: 1)
nrep	Number of independent replicates (default: 1)
local	Logical for local kriging (default: FALSE)
L	Number of lines for turning bands method (default: 1000)
neighb	Optional neighborhood order for local kriging
param	List of parameter values
anisopars	List with anisotropy angle and ratio
radius	Radius for spherical coordinates (default: Earth's radius)
sparse	Logical for sparse matrix algorithms (default: FALSE)
time	Optional vector of temporal instants to predict
copula	Optional string specifying copula type
X	Optional matrix of spatio-temporal covariates
Xloc	Optional matrix of covariates for prediction locations
Mloc	Optional vector of estimated means for prediction locations
parallel	If TRUE the the computation is parallelized
ncores	Numbers of cores involved in the parallelization

Details

For Gaussian RF, performs conditional simulation using three steps:

1. Unconditional simulation at observed and prediction locations

2. Simple kriging estimates at observed locations

3. Combination to produce conditional simulations

For large datasets, approximate methods ("TB" or "CE") are recommended coupled with local kriging (local=TRUE and neighb=k) and using parallelization (parallel=T).

Value

Returns an object of class GeoSimcond containing:

• Simulated field realizations

• Model parameters and settings

• Spatial/temporal coordinates

• Covariance information

Author(s)

Moreno Bevilacqua [e-mail],\ Víctor Morales Oñate [e-mail],\ Christian Caamaño-Carrillo [e-mail]

References

Gaetan, C. and Guyon, X. (2010) Spatial Statistics and Modelling. Springer Verlag, New York.

See Also

GeoSim, GeoKrig

Examples

library(GeoModels)

##############################################
## conditional simulation of a Gaussian rf ###
##############################################
model="Gaussian"
set.seed(79)
### conditioning locations
x = runif(250, 0, 1)
y = runif(250, 0, 1)
coords=cbind(x,y)

# Set the exponential cov parameters:
corrmodel = "GenWend"
mean=0; sill=1; nugget=0
scale=0.2;smooth=0;power2=4

param=list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth,power2=power2)

# Simulation 
data = GeoSim(coordx=coords, corrmodel=corrmodel,model=model,
              param=param)$data

## estimation with pairwise likelihood
fixed=list(nugget=nugget,smooth=smooth,power2=power2)
start=list(mean=0,scale=scale,sill=1)
I=Inf
lower=list(mean=-I,scale=0,sill=0)
upper=list(mean= I,scale=I,sill=I)
# Maximum pairwise likelihood fitting :
fit = GeoFit(data, coordx=coords, corrmodel=corrmodel,model=model,
             likelihood='Marginal', type='Pairwise',neighb=3,
             optimizer="nlminb", lower=lower,upper=upper,
             start=start,fixed=fixed)

# locations to simulate 
xx=seq(0,1,0.025)
loc_to_sim=as.matrix(expand.grid(xx,xx))

# Conditional simulation
sim_result <- GeoSimcond(fit,loc = loc_to_sim,nrep=5)$condsim
sim_result <- do.call(rbind, sim_result)

par(mfrow=c(1,2))
quilt.plot(coords, data)
quilt.plot(loc_to_sim, colMeans(sim_result))
par(mfrow=c(1,1))

##############################################
## conditional simulation of a LogGaussian rf 
##############################################
model="LogGaussian"
set.seed(79)
### conditioning locations
x = runif(250, 0, 1)
y = runif(250, 0, 1)
coords=cbind(x,y)

# Set the exponential cov parameters:
corrmodel = "Matern"
mean=0; sill=.1; nugget=0
scale=0.2;smooth=0.5

param=list(mean=mean,sill=sill,nugget=nugget,scale=scale,smooth=smooth)

# Simulation 
data = GeoSim(coordx=coords, corrmodel=corrmodel,model=model,
              param=param)$data

## estimation with pairwise likelihood
fixed=list(nugget=nugget,smooth=smooth)
start=list(mean=0,scale=scale,sill=1)
I=Inf
lower=list(mean=-I,scale=0,sill=0)
upper=list(mean= I,scale=I,sill=I)
# Maximum pairwise likelihood fitting :
fit = GeoFit(data, coordx=coords, corrmodel=corrmodel,model=model,
             likelihood='Marginal', type='Pairwise',neighb=3,
             optimizer="nlminb", lower=lower,upper=upper,
             start=start,fixed=fixed)

# locations to simulate 
xx=seq(0,1,0.025)
loc_to_sim=as.matrix(expand.grid(xx,xx))

# Conditional simulation
sim_result <- GeoSimcond(fit,loc = loc_to_sim,nrep=5)$condsim
sim_result <- do.call(rbind, sim_result)

par(mfrow=c(1,2))
quilt.plot(coords, data)
quilt.plot(loc_to_sim, colMeans(sim_result))
par(mfrow=c(1,1))

GeoModels documentation built on June 8, 2025, 11:45 a.m.